A Two Stage Classification Model for Call Center Purchase Prediction
Abstract: In call center [1]
product recommendation field, call center as an organization between users and telecom
operator, doesn’t have permission to access users’ specific information and the
detailed products information. Accordingly, rule-based selection method is
common used to predict user purchase behavior by the call center. Unfortunately,
rule-based approach not only ignores the user’s previous behavior information
entirely, and it is difficult to make use of the existing interaction records
between users and products. Consequently, it will not get desired results if we
just use the basic selection method to predict user purchase behavior directly,
because the problem is that the features straightly extracted from theinteraction
data records are limited. In order to solve the problem above, this paper
proposes a two-stagealgorithm that based on K-Means Clustering Algorithm [2]
and SVM [3, 4] Classification Algorithm. Firstly,we get the potential category
information of products by K-Means Clustering Algorithm, and then use SVM Classification
Model to predict users purchasing behavior. This two-stage prediction model not
only solves the feature shortage problem, but also gives full consideration to
the potential features between users and product categories, which can help us
to gain significant performance in call center product recommendation field.
Keywords: call center,
K-Means, purchase prediction, SVM
Author: Kai Shuang, Kai-Ze
Ding, Xi-Hao Liu, Xiao-Le Wen
Journal Code: jptkomputergg170033
